Completed Review of Various Solar Power Forecasting Techniques Considering Different Viewpoints

Wu, Yuan‐Kang; Huang, Cheng‐Liang; Phan, Quoc-Thang; Li, Yuan-Yao

doi:10.3390/en15093320

Cited by 38 publications

(13 citation statements)

References 94 publications

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“…PV power forecasting is a topic widely investigated due to its economic and ecologic impact [2], [9], [10], [11], [12], [13], [14], [15], [16], [17]. Research by Wan et.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Power Generation of a Photovoltaic Power Plant Based on Neural Networks

et al. 2023

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Photovoltaic energy production is an important factor for increasing the electricity supply. The ability to predict the electric power production (EPP) of a photovoltaic (PV) farm supports from the management process of the power grid to the trade in the energy market and much more. Also, by predicting the production of PV power (PVP), it is possible to monitor the lifetime of the solar cells that form the backbone of any solar PV system. As a critical result, sudden failures of the PV plant can be avoided. Using a long short-term memory recurrent neural network (LSTM-RNN) model, this work evaluates the prediction accuracy of two forecasting strategies: the recursive strategy and the non-recursive Multiple-Input and Multiple-Output, respectively. The dataset consists of 5-years in-filed production data measurements collected from the CETATEA photovoltaic power plant, a research site facility for renewable energies located in Cluj-Napoca, Romania. The high granularity of the electric power production dataset values recorded each 1 hour guarantees the overall prediction accuracy of the system. The impact of the dataset size, the number of previous observations, and the forecast horizon on the neural network prediction accuracy is evaluated for each strategy. The performance metrics used to evaluate the prediction accuracy are the root mean square error, the mean bias error, and the mean average error. The results analysis demonstrates the ability of the implemented machine learning models to predict electric power production, as well as their importance in the energy loss management process.

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“…PV power forecasting is a topic widely investigated due to its economic and ecologic impact [2], [9], [10], [11], [12], [13], [14], [15], [16], [17]. Research by Wan et.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Power Generation of a Photovoltaic Power Plant Based on Neural Networks

et al. 2023

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“…Wu et al [1] provided a comprehensive review of various solar power forecasting techniques, including ANNs and PRs. The authors discussed the advantages and disadvantages of the techniques and highlighted the factors that affect their accuracy.…”

Section: Literature Surveymentioning

confidence: 99%

“…tilted planes (I 3 ) and (I 15 ). The dataset also consists of the hourly mean PV power plant output (P) of total 960 kW solar plant [1].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Solar PV Plant output using Polynomial Regression and ANN Algorithm

Kulkarni,

Kakade,

Mawande

et al. 2023

J. Phys.: Conf. Ser.

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The paper proposes the use of Artificial Neural Networks (ANN) and Polynomial Regression (PR) algorithms for estimating the output of a Solar Photovoltaic (PV) Plant. The study compares the performance of these two methods, using a dataset of weather parameters and PV Plant output. The results show that both the methods can effectively estimate the PV Plant output, with the ANN model having a slightly higher accuracy than the polynomial regression model as ANNs can learn the nonlinear relationship between input stochastic parameters and output in a better manner. The study highlights the potential of machine learning techniques for optimizing the performance of solar PV plants and improving their efficiency.

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“…The study [ 64 ] reveals that the output power with the insolation and the air temperature has a linear and nonlinear correlation, correspondingly. Recently, researchers have been more interested in the ML application to increase the accuracy of the forecasters [ 61 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 ].…”

Section: Machine Learning Applications For a Solar Plant Systemmentioning

confidence: 99%

A Review on Machine Learning Applications for Solar Plants

Engel

2022

Sensors

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A solar plant system has complex nonlinear dynamics with uncertainties due to variations in system parameters and insolation. Thereby, it is difficult to approximate these complex dynamics with conventional algorithms whereas Machine Learning (ML) methods yield the essential performance required. ML models are key units in recent sensor systems for solar plant design, forecasting, maintenance, and control to provide the best safety, reliability, robustness, and performance as compared to classical methods which are usually employed in the hardware and software of solar plants. Considering this, the goal of our paper is to explore and analyze ML technologies and their advantages and shortcomings as compared to classical methods for the design, forecasting, maintenance, and control of solar plants. In contrast with other review articles, our research briefly summarizes our intelligent, self-adaptive models for sizing, forecasting, maintenance, and control of a solar plant; sets benchmarks for performance comparison of the reviewed ML models for a solar plant’s system; proposes a simple but effective integration scheme of an ML sensor solar plant system’s implementation and outlines its future digital transformation into a smart solar plant based on the integrated cutting-edge technologies; and estimates the impact of ML technologies based on the proposed scheme on a solar plant value chain.

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Completed Review of Various Solar Power Forecasting Techniques Considering Different Viewpoints

Cited by 38 publications

References 94 publications

Prediction of Power Generation of a Photovoltaic Power Plant Based on Neural Networks

Prediction of Power Generation of a Photovoltaic Power Plant Based on Neural Networks

Estimation of Solar PV Plant output using Polynomial Regression and ANN Algorithm

A Review on Machine Learning Applications for Solar Plants

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